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Advances in Meteorology
Volume 2016, Article ID 3103749, 13 pages
http://dx.doi.org/10.1155/2016/3103749
Research Article

Correction of TRMM 3B42V7 Based on Linear Regression Models over China

China Institute of Water Resources and Hydropower Research (IWHR), State Key Laboratory of Simulation and Regulation of Water Cycle in River Basin, Beijing 100038, China

Received 4 August 2016; Revised 7 October 2016; Accepted 7 November 2016

Academic Editor: Pedro Jiménez-Guerrero

Copyright © 2016 Shaohua Liu et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

High temporal-spatial precipitation is necessary for hydrological simulation and water resource management, and remotely sensed precipitation products (RSPPs) play a key role in supporting high temporal-spatial precipitation, especially in sparse gauge regions. TRMM 3B42V7 data (TRMM precipitation) is an essential RSPP outperforming other RSPPs. Yet the utilization of TRMM precipitation is still limited by the inaccuracy and low spatial resolution at regional scale. In this paper, linear regression models (LRMs) have been constructed to correct and downscale the TRMM precipitation based on the gauge precipitation at 2257 stations over China from 1998 to 2013. Then, the corrected TRMM precipitation was validated by gauge precipitation at 839 out of 2257 stations in 2014 at station and grid scales. The results show that both monthly and annual LRMs have obviously improved the accuracy of corrected TRMM precipitation with acceptable error, and monthly LRM performs slightly better than annual LRM in Mideastern China. Although the performance of corrected TRMM precipitation from the LRMs has been increased in Northwest China and Tibetan plateau, the error of corrected TRMM precipitation is still significant due to the large deviation between TRMM precipitation and low-density gauge precipitation.